Nanotechnology and artificial intelligence to enable sustainable and precision agriculture

Abstract

Climate change, increasing populations, competing demands on land for production of biofuels, and declining soil quality are challenging global food security. Finding sustainable solutions requires bold new approaches and integration of knowledge from diverse fields, such as materials science and informatics. The convergence of precision agriculture, whereby farmers respond in real-time to changes in crop growth, with nanotechnology and artificial intelligence offers exciting opportunities for sustainable food production. Coupling existing models for nutrient cycling and crop productivity with nanoinformatics approaches to optimize targeting, uptake, delivery nutrient capture and long term impacts on soil microbial communities will allow design of nanoscale agrochemcials that combine optimal safety and functionality profiles. bacterial community structure after just 90 days of exposure to a realistic concentration of NPs (1 mg kg −1 dry soil) 10 , while studies with Ag NMs, which are well-known for their antimicrobial activity have shown that the extent of impact on soil community composition over 90 days are affected by exposure time and physicochemical composition of soil as well as the type and coating of the NMs 11 . Thus, an important caveat at the outset of this review is that NMs represent a very broad spectrum of chemistries, compositions and physicochemical properties, which are dynamic and evolving as the NMs interact with their surroundings, and as such generalisations regarding their applications in agriculture are difficult, and predictions of long-term effects are challenging currently.However, as noted in the aforementioned reviews 3, 4, 5 , the development of nanotechnology for agricultural applications is still at an early stage and is moving forward quite slowly. Significant differences may exist between nanotechnology-based pesticides and conventional pesticides, including altered bioavailability, sensitivity, dosimetry, and pharmacokinetics 12, 13 . Challenges and barriers include limited understanding of plant-NMs interactions, limited methods for efficient delivery of NMs to plants and soil, risks of potentially hazardous effects of NMs to human health from accumulation of NMs and active ingredient residues in edible portions of plants 4 , and to long term soil quality and soil health from accumulation of NMs and their degradation products in soil and resultant potential alterations in microbial biodiversity 14 . There is an urgent need to address these barriers and achieve a true win-win scenario, whereby improved agricultural production, reduced environmental pollution from agriculture and lower costs for farmers can be achieved synergistically. A one-health approach to nano-agriculture was proposed by Lombi et al., that requires interdisciplinarity and the bridging of human and environmental health research 15 . Computational approaches including artificial intelligence (A.I.) and machine learning (M.L.) modelling will undoubtedly play critical roles in the progess of nano-enabled agric...

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“…The use of digital technologies to transform agri-food systems is often referred to as the fourth agricultural revolution and is characterized by "high-tech, radical, and potentially game-changing technologies" [2]. The digitalization of agricultural systems is aimed at the technological optimization of production, value chains, and food systems, as well as minimizing the environmental impacts of agriculture [5,6]. Artificial intelligence (AI) is one innovation emerging from the digitalization trend, often being used for precision agriculture and to enhance smart farming techniques [6].…”

“…The digitalization of agricultural systems is aimed at the technological optimization of production, value chains, and food systems, as well as minimizing the environmental impacts of agriculture [5,6]. Artificial intelligence (AI) is one innovation emerging from the digitalization trend, often being used for precision agriculture and to enhance smart farming techniques [6].…”

“…The benefits of integrating AI into agricultural production abound, ranging from improving the traceability of food-related outbreaks, improving hygiene on production sites, integrating supply chains, reducing waste related to production, reducing the agricultural sector's carbon and ecological footprint, and increasing economic profitability [9]. The motivation underlying AI adoption in agriculture is the idea of using innovation to optimize production systems to feed an increasing global population while simultaneously conserving per capita agricultural land area and preserving soil health and environmental quality [6,11].…”

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